Part 4 - NLP | DL Industry Level Project - Inference Pipeline | Deployment Setup

In this video, we complete the final stage of the industry-level NLP project by building the inference pipeline and preparing the system for real-world usage. This is Part 4 of the series where we load the trained model, set up the prediction pipeline, and automatically forward customer complaints to the appropriate department using email routing. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. In the previous parts of this project, we completed the full machine learning pipeline including data gathering, exploratory data analysis, preprocessing, tokenization, embedding, model building, and training the LSTM model for complaint classification. Now we focus on the production side of the system. The first step is building the inference pipeline. During training, the model learned patterns based on a specific preprocessing pipeline and tokenizer vocabulary. Therefore, the same preprocessing steps must be applied during inference. This includes cleaning incoming complaint text, removing unwanted characters, converting text to lowercase, tokenizing the complaint using the saved tokenizer, and padding the sequence to match the input shape expected by the trained model. Next we load the trained model and tokenizer that were saved during the training stage. The trained model is loaded using TensorFlow Keras, and the tokenizer is loaded using pickle so that the same vocabulary mapping is preserved. This ensures that the input text is converted into the same token IDs that the model was trained on. Once the preprocessing and model loading steps are ready, we build the inference function. This function takes a raw complaint text as input, processes the text using the preprocessing pipeline, converts it into padded token sequences, and feeds it to the trained LSTM model. The model outputs probability scores for each department category. The class with the highest probability